TY  - GEN
SN  - 1554-7868
UR  - https://doi.org/10.1109/ISMAR.2018.00024
A1  - Runz, M
A1  - Buffier, M
A1  - Agapito, L
SP  - 10
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10074870
N2  - We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene. MaskFusion recognizes, segments and assigns semantic class labels to different objects in the scene, while tracking and reconstructing them even when they move independently from the camera. As an RGB-D camera scans a cluttered scene, image-based instance-level semantic segmentation creates semantic object masks that enable realtime object recognition and the creation of an object-level representation for the world map. Unlike previous recognition-based SLAM systems, MaskFusion does not require known models of the objects it can recognize, and can deal with multiple independent motions. MaskFusion takes full advantage of using instance-level semantic segmentation to enable semantic labels to be fused into an object-aware map, unlike recent semantics enabled SLAM systems that perform voxel-level semantic segmentation. We show augmented-reality applications that demonstrate the unique features of the map output by MaskFusion: instance-aware, semantic and dynamic. Code will be made available.
PB  - IEEE
CY  - Munich, Germany
KW  - Visual SLAM
KW  -  SLAM
KW  -  Visualization
KW  -  Tracking
KW  -  Mapping
KW  -  Fusion
KW  -  RGBD
KW  -  Multi-object Recognition
KW  -  Context
KW  -  Semantic
KW  -  Detection Real-time
KW  -  Augmented-Reality
KW  -  Robotics
KW  -  3D
KW  -  SEGMENTATION
KW  -  LOCALIZATION
T3  - International Symposium on Mixed and Augmented Reality
TI  - MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
EP  - 20
Y1  - 2019/01/17/
AV  - public
ER  -